{"id":"W4311099598","doi":"10.1186/s42400-022-00125-w","title":"Deep 3D mesh watermarking with self-adaptive robustness","year":2022,"lang":"en","type":"article","venue":"Cybersecurity","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Polygon mesh; Digital watermarking; Robustness (evolution); Computer science; Watermark; Mesh networking; Embedding; Distributed computing; Theoretical computer science; Artificial intelligence; Computer graphics (images); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003577918,0.0005249172,0.0003876721,0.0005322004,0.0001905732,0.0004593269,0.0006743859,0.0007957125,0.001087901],"category_scores_gemma":[0.001237226,0.0002269919,0.0004817698,0.0003325627,0.0005700861,0.001264737,0.001165336,0.0006121566,0.0002960818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035333,"about_ca_system_score_gemma":0.0002151971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006963949,"about_ca_topic_score_gemma":0.0007968621,"domain_scores_codex":[0.9997609,0.00003808709,0.00001407733,0.00004828574,0.0001067151,0.0000319825],"domain_scores_gemma":[0.9996141,0.0001035359,0.0000706436,0.0001192299,0.00007137503,0.00002109226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002885865,0.00007798768,0.001006645,0.0001218494,0.00007693222,0.0002692016,0.00008758929,0.4356424,0.2529932,0.01656899,0.00160606,0.2912605],"study_design_scores_gemma":[0.000005198221,0.00003695808,0.0001472763,0.000004720282,0.000008965793,0.00005371699,0.00000512178,0.9744571,0.02227468,0.002302998,0.0006959674,0.000007219685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05673464,0.0002229731,0.9401589,0.0001355305,0.0000457424,0.00002593786,0.00003670216,0.0007850475,0.001854637],"genre_scores_gemma":[0.8447377,0.0002832266,0.1513274,0.0001212006,0.00004671726,0.00003940307,0.0001163378,0.00009362902,0.003234253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001087901,"threshold_uncertainty_score":0.0036394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009041393914900246,"score_gpt":0.2118992292506767,"score_spread":0.2028578353357764,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}